Question

此代码用于显示延迟交货的时间，打印出与之相关的“物料”编号，并显示延迟交货的天数。我的问题现在在于尝试过滤数据集以仅读取指定的时间范围；在我的以下代码中，我尝试过滤2017年至2018年的数据，但是我收到一个错误（在代码块下方列出）。在进行相同的分析时，如何过滤行以仅显示指定的时间范围：这是查看哪些材料零件号交货延迟，并查看它延迟了几天（没有出错）

import pandas as pd
from datetime import datetime
from datetime import timedelta



df = pd.read_csv('otd.csv')

diff_delivery_date = []
date_format = '%m/%d/%Y'
df2 = df[(df['Delivery Date'].dt.year >= 2017) & (df['Delivery Date'].dt.year <= 2018)]



for x,y,z in zip(df2['Material'], df2['Delivery Date'], df2['source desired delivery date']):
    actual_deliv_date = datetime.strptime(y, date_format)
    supposed_deliv_date = datetime.strptime(z, date_format)
    diff_deliv_date = supposed_deliv_date - actual_deliv_date
    diff_delivery_date.append(diff_deliv_date)

df['Diff Deliv Date'] = diff_delivery_date

print(df2)

完全错误：

Traceback (most recent call last):
  File "C:\Users\khalha\eclipse-workspace\Python\Heyy\Code.py", line 13, in <module>
    df2 = df[(df['Delivery Date'].dt.year >= 2017) & (df['Delivery Date'].dt.year <= 2018)]
  File "C:\Users\khalha\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\generic.py", line 4372, in __getattr__
    return object.__getattribute__(self, name)
  File "C:\Users\khalha\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\accessor.py", line 133, in __get__
    accessor_obj = self._accessor(obj)
  File "C:\Users\khalha\AppData\Local\Programs\Python\Python37\lib\site-packages\pandas\core\indexes\accessors.py", line 325, in __new__
    raise AttributeError("Can only use .dt accessor with datetimelike "
AttributeError: Can only use .dt accessor with datetimelike values

虚拟csv： Image of csv file

Material    Delivery Date   source desired delivery date
3334678 12/31/2014  12/31/2014
233433  12/31/2014  12/31/2014
3434343 1/5/2015    1/5/2015
3334567 1/5/2015    1/5/2015
546456  2/11/2015   2/11/2015
221295  4/10/2015   4/10/2015

示例数据框：

Deliveryvalue = df2['11/31/2014', '11/31/2017', '11/31/2018']
Desiredvalue = df2['12/31/2014', '12/21/2017', '12/11/2018']

Answer 1

这个答案是我假设您的数据具有以下格式：

Material,Delivery Date,source desired delivery date
3334678,12/31/2017,12/31/2017
233433,12/31/2017,12/31/2017
3434343,1/5/2017,1/5/2017
3334567,1/5/2017,1/5/2017
546456,2/11/2017,2/11/2017
221295,4/10/2017,4/10/2017

因此，假设您可以这样做：

import pandas as pd

df = pd.read_csv('odt.csv')

df['Delivery Date'] = pd.to_datetime(df['Delivery Date'], format='%m/%d/%Y')
df['source desired delivery date'] = pd.to_datetime(df['source desired delivery date'], format='%m/%d/%Y')

df2 = df[(df['Delivery Date'].dt.year >= 2017) & (df['Delivery Date'].dt.year <= 2018)]
df2['Diff Deliv Date'] = df2['Delivery Date'] - df2['source desired delivery date']

print(df2)

输出

   Material Delivery Date source desired delivery date Diff Deliv Date
0   3334678    2017-12-31                   2017-12-31          0 days
1    233433    2017-12-31                   2017-12-31          0 days
2   3434343    2017-01-05                   2017-01-05          0 days
3   3334567    2017-01-05                   2017-01-05          0 days
4    546456    2017-02-11                   2017-02-11          0 days
5    221295    2017-04-10                   2017-04-10          0 days

注释

加载数据后，列的类型如下：

Material                         int64
Delivery Date                   object
source desired delivery date    object

您可以检查您是否属于这些人。然后，您需要将'Delivery Date'和'source desired delivery date'转换为日期时间，这是在以下位置完成的：

df['Delivery Date'] = pd.to_datetime(df['Delivery Date'], format='%m/%d/%Y')
df['source desired delivery date'] = pd.to_datetime(df['source desired delivery date'], format='%m/%d/%Y')

然后简单地过滤数据并计算差值。我也改变了：

df['Diff Deliv Date'] = diff_delivery_date

给出的df2比代码最后显示的df2。

按年份过滤行； AttributeError：只能将.dt访问器与类似datetime的值一起使用

1 个答案: